Pixel Change Research Articles

Secure and covert communication using steganography by Wavelet Transform

This paper presents a hybrid image steganography along with cryptography algorithm based on LS (Logical & Statistical) encryption scheme and lift wavelet domain of digital images. Currently, cryptography data can be easily intercepted by an intruder during transmission. So it may be treated as ineffectual in terms of security. In such a scenario, the proposed algorithm provides secured transmission by using cryptography and steganography techniques. In this algorithm a new LS encryption scheme is implemented for converting secret message into ciphertext. It is being done by using several parameters like 'data indicator', 'count indicator', 'gray code' and novel 'data conversion' techniques. The 'gray code' and novel 'data conversion' techniques are used to recover the inherent information at the receiver and to improve the security of the secret data. Moreover, it reduces the ciphertext length compared to the length of original secret data. Hence this scheme improves the payload capacity and helps to improve the quality metrics like Number of Pixels Change in Rate (NPCR), Peak Signal to Noise Ratio (PSNR), and Correlation (CORR) in the stego image. Furthermore, steganography scheme provides an ability to tackle the various attacks like noise, rotational, histogram and visual attacks. It is also shown that how the original message is recovered back from stego image successfully.

Land Consumption Monitoring with SAR Data and Multispectral Indices

Land consumption is the increase in artificial land cover, which is a major issue for environmental sustainability. In Italy, the Italian Institute for Environmental Protection and Research (ISPRA) and National System for Environmental Protection (SNPA) have the institutional duty to monitor land consumption yearly, through the photointerpretation of high-resolution images. This study intends to develop a methodology in order to produce maps of land consumption, by the use of the semi-automatic classification of multitemporal images, to reduce the effort of photointerpretation in detecting real changes. The developed methodology uses vegetation indices calculated over time series of images and decision rules. Three variants of the methodology were applied to detect the changes that occurred in Italy between the years 2018 and 2019, and the results were validated using ISPRA official data. The results show that the produced maps include large commission errors, but thanks to the developed methodology, the area to be photointerpreted was reduced to 7300 km2 (2.4% of Italian surface). The third variant of the methodology provided the highest detection of changes: 70.4% of the changes larger than 100 m2 (the pixel size) and over 84.0% of changes above 500 m2. Omissions are mainly related to single pixel changes, while larger changes are detected by at least one pixel in most of the cases. In conclusion, the developed methodology can improve the detection of land consumption, focusing photointerpretation work over selected areas detected automatically.

Chaotic System and DNA Computing operations for Image Encryption Based on Pixels Shuffling

Image encryption is one of the primary approaches which is used to keep image information secure and safe. Recently, image encryption is turning its attention to combination with the field of DNA computing. In the presented study, a novel method of image encryption is suggested and implemented based on the DNA algorithm and Chaos theory, the most important principle in image encryption is breaking the correlation amongst pixels. This algorithm performs well against chosen cipher-text attacks. Furthermore, the proposed approach was implemented and analyzed for the Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), The performance of the encryption method is analyzed using the histogram, Shannon entropy and key space.

A Symmetric Key Multiple Color Image Cipher Based on Cellular Automata, Chaos Theory and Image Mixing

The transmission of significant masses of sensitive and secret images over a public network is inevitable, and demands effective tools and technology to safeguard and conceal the data. In this paper, a symmetric multiple color image encryption technique is proposed by adopting a dual permutation and dual substitution framework. Firstly, the input images are combined into a large image and then segmented into many small and equal-sized pure-image elements. Secondly, using the elementary cellular automata Rule-30, these pure-image elements are permuted to obtain mixed-image elements. Thirdly, second-level permutation is undertaken on the mixed-image elements by applying zigzag pattern scanning. Fourthly, pixel values are substituted by employing the circular shift method; subsequently, second-level pixel substitution is realized through using chaotic random sequences from a 2D logistic map. Finally, the big encrypted image is segmented into smaller encrypted images. Additionally, the keys are calculated from the input images to attain input sensitivity. The efficiency of this method is quantified, based on the unified average changing intensity (UACI), information entropy, number of pixels change rate (NPCR), key sensitivity, key space, histogram, peak signal-to-noise ratio (PSNR) and correlation coefficient (CC) performance metrics. The outcome of the experiments and a comparative analysis with two similar methods indicate that the proposed method produced high security results.

An innovative technique for image encryption using tri-partite graph and chaotic maps

In this article, the concept of a complete 3-Partite graph is used to substitute the pixels of a color image to achieve encryption. Chen’s chaotic system is deployed to generate vertices of a tri-partite directed graph and then paths are established between these vertices. These paths will serve to perform the substitution of individual pixels. The seeds for the chaotic maps are manipulated by the hash value of the plain image to hinder chosen plaintext attack. The inter and intra correlation of pixels of a three color image in one-dimensional vector is distorted using the non-linear system of equations. The different chaotic maps having divergent features are employed to add up complexity in encryption technique. The extensive experiments are applied on different kinds of images and results are compiled. The results show that the proposed system has a strong robustness against different attacks and prove to be an excellent candidate to encrypt colored digital images. It has high scores for the Number of Pixel Change Rate (NPCR), Unified Average Cipher Intensity (UACI), entropy values, low values for Mean Absolute Error (MAE), Chi-Square and variance of histograms for encrypted images. The palpable lead of proposed system has resilience against transmission impairments such as Gaussian, Salt & Pepper and clipping.

A New Algorithm for Digital Image Encryption Based on Chaos Theory.

In recent decades, image encryption, as one of the significant information security fields, has attracted many researchers and scientists. However, several studies have been performed with different methods, and novel and useful algorithms have been suggested to improve secure image encryption schemes. Nowadays, chaotic methods have been found in diverse fields, such as the design of cryptosystems and image encryption. Chaotic methods-based digital image encryptions are a novel image encryption method. This technique uses random chaos sequences for encrypting images, and it is a highly-secured and fast method for image encryption. Limited accuracy is one of the disadvantages of this technique. This paper researches the chaos sequence and wavelet transform value to find gaps. Thus, a novel technique was proposed for digital image encryption and improved previous algorithms. The technique is run in MATLAB, and a comparison is made in terms of various performance metrics such as the Number of Pixels Change Rate (NPCR), Peak Signal to Noise Ratio (PSNR), Correlation coefficient, and Unified Average Changing Intensity (UACI). The simulation and theoretical analysis indicate the proposed scheme’s effectiveness and show that this technique is a suitable choice for actual image encryption.

Color image encryption based on joint permutation and diffusion

Image encryption plays an essential role in the community of image security. Most existing image encryption approaches adopt a permutation–diffusion scheme to permute pixel positions and change pixel values separately. One limitation of this scheme is that it has a high risk of being cracked. To solve this problem, we propose an approach that jointly permutates and diffuses (JPD) the pixels in a color image for encryption. First, a 4D hyperchaotic system with two positive Lyapunov exponents is used to generate a sequence for almost all encryption procedures. To enhance security, the plain image’s information is introduced to the hyperchaotic system’s initial parameters. Then, the hyperchaotic sequence is used to permute and diffuse the pixels in images jointly. More specifically, two index matrices that determine which pixels will be permuted and diffused and one mask matrix that determines how the pixels will be diffused are generated by the hyperchaotic sequence. We test the proposed JPD with several popular images. Experimental results and security analysis demonstrate that the JPD is capable of resisting various types of attacks. Moreover, the JPD can accelerate the encryption process. All these indicate that the JPD is effective and efficient for color image encryption.

Geolocation Error Estimation and Correction on Long-Term MWRI Data

Due to the limitation of the satellite attitude measurement accuracy and the system servo control error of the payload scanning mechanism, an optimal use of Micro-Wave Radiation Imager (MWRI) observations requires high geolocation accuracy. In the operational system, the MWRI geolocation accuracy reaches 1 pixel, and there still exists room for improvement. In this article, we improve upon the coastline inflection point method (CIM) and propose to assign the accurate correspondence by employing a nonrigid point set registration method. First, the method identifies a set of latent variables to recognize outliers and then applies nonparametric geometric constraints to the correspondence as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> distribution. Second, the maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) estimation is applied by the expectation–maximization (EM) algorithm to obtain correct inliers. The comparison with other methods demonstrates that the proposed method can provide more accurate estimation of geolocation bias. In addition, the pixel error and changes in spacecraft attitude with the long-term geolocation data in FY-3C MWRI before and after correction were analyzed during the period from April 1 to August 30, 2018. The results have shown that the geolocation errors are reduced from [0.50, 0.60] pixels to [0.20, 0.33] pixels in the along- and cross-track directions after the attitude correction. In addition, the reduction of the standard deviation shows that the geolocation quality of MWRI is improved.

Backdoors hidden in facial features: a novel invisible backdoor attack against face recognition systems

Deep neural network (DNN) based face recognition system has become one of the most popular modalities for user identity authentication. However, some recent studies have indicated that, the malicious attackers can inject specific backdoors into the DNN model of a face recognition system, which is known as backdoor attack. As a result, the attacker can trigger the backdoors and impersonate someone else to log into the system, while not affecting the normal usage of the legitimate users. Existing studies use the accessories (such as purple sunglasses or bandanna) as the triggers of their backdoor attacks, which are visually conspicuous and can be easily perceptible by humans, thus result in the failure of backdoor attacks. In this paper, for the first time, we exploit the facial features as the carriers to embed the backdoors, and propose a novel backdoor attack method, named BHF2 (Backdoor Hidden in Facial Features). The BHF2 constructs the masks with the shapes of facial features (eyebrows and beard), and then injects the backdoors into the masks to ensure the visual stealthiness. Further, to make the backdoors look more natural, we propose BHF2N (Backdoor Hidden in Facial Features Naturally) method, which exploits the artificial intelligence (AI) based tool to auto-embed the natural backdoors. The generated backdoors are visually stealthy, which can guarantee the concealment of the backdoor attacks. The proposed methods (BHF2 and BHF2N) can be applied for those black-box attack scenarios, in which a malicious adversary has no knowledge of the target face recognition system. Moreover, the proposed attack methods are feasible for those strict identity authentication scenarios where the accessories are not permitted. Experimental results on two state-of-the-art face recognition models show that, the maximum success rate of the proposed attack method reaches 100% on DeepID1 and VGGFace models, while the accuracy degradation of target recognition models are as low as 0.01% (DeepID1) and 0.02% (VGGFace), respectively. Meantime, the generated backdoors can achieve visual stealthiness, where the pixel change rate of a backdoor instance relative to its clean face image is as low as 0.16%, and their structural and dHash similarity score are high up to 98.82% and 98.19%, respectively.

Patchwise dictionary learning for video forest fire smoke detection in wavelet domain

Fire smoke detection in forest faces much more challenges than in local areas or indoors. Conventional fire smoke methods using static features extracted form single image directly cannot handle some disturbed factors exist in forest, such as shaking trees and fog/haze. On the other hand, it is difficult to capture the dynamic features of fire smoke while they are vital for fire smoke detection. This paper proposes an effective method of extracting features based on sequential images for smoke detection, quasi-dynamic features are extracted using on pixel-block arranged image sets. First, consecutive frames are rearranged into a new big image based on a pixel-block rule, so that the new image still keep the whole structure of original image on block level, and the change of different pixels in the same block, which comes from the same position of each frame, can reflect the dynamic features by special process. Second, to process the new arranged big image, driven by single image wavelet transform for denoting local signal characteristics, Dual Tree-Complex Wavelet Transform is utilized to decompose the new big image into eight images with the same size of the source image after several level-decomposition, including four directions (HH, HL, LH, LL) with two decomposed images in each direction, so that the changes from smoke in different orientation can be extracted and depicted in these subbands. Third, a multi-scale dictionary learning method is proposed to learn dictionaries of each group images. Furthermore, elastic net is introduced for sparse coding and feature coefficients extraction. All operations above contribute to a significant improvement for smoke detection in this paper. Extensive experiments are performed to validate the effectiveness of the proposed approach.

Robustness Augmentation of Deep Learning Model Based on Pixel Change

Deep learning has been widely used in many fields. A large number of images can be quickly recognized by the deep learning models to provide information. How to improve the robustness of deep learning applications has become the focus of research. Unfortunately, the recognition ability of the existing deep learning model has been greatly threatened, many images can cause recognition errors in a well-trained model. Although data augmentation is an effective method, the existence of adversarial examples shows that traditional data augmentation methods have no obvious effect on minor pixel changes. After analyzing the impact of pixel changes on model recognition accuracy, a data augmentation method based on a small number of pixel changes is proposed. Our method can optimize the corresponding classification boundary and improve the recognition robustness of the model. Finally, a simple evaluation method to measure the robustness of model recognition is proposed. Our experiments prove the threat of a small number of pixels and the effectiveness of our data augmentation method. Moreover, the data augmentation method has strong generalization ability and can be applied to image recognition in many different fields.

Low-Latency and Scene-Robust Optical Flow Stream and Angular Velocity Estimation

Event cameras are bio-inspired sensors that capture intensity changes of pixels individually, and generate asynchronous and independent “events”. Due to the fundamental difference from the conventional cameras, most research on event cameras builds a global event frame by grouping events according to their timestamps or their number to employ traditional computer vision algorithms. However, in order to take advantage of event cameras, it makes sense to generate asynchronous output on an event-by-event basis. In this paper, we propose an optical flow estimation algorithm with low latency and robustness to various scenes to utilize the advantage of the event camera by enhancing the existing optical flow algorithm. Furthermore, we estimate angular velocity with low latency using the proposed optical flow stream. For the validation of algorithms, we evaluate the accuracy and latency of optical flow with publicly available datasets. Moreover, we assess the performance of the proposed angular velocity estimation in comparison to the existing algorithms. Both validations suggest that our asynchronous optical flow shows comparable accuracy to the existing algorithms and the latency is reduced by half compared to the existing block matching algorithm on average. Also, our angular velocity estimation is superior to the existing algorithms in terms of accuracy and robustness while showing low latency within 15 ms consistently.

Auto-Tuned Event-Based Perception Scheme for Intrusion Monitoring With UAS

This paper presents an asynchronous event-based scheme for automatic intrusion monitoring using Unmanned Aerial Systems (UAS). Event cameras are neuromorphic sensors that capture the illumination changes in the camera pixels with high temporal resolution and dynamic range. In contrast to conventional frame-based cameras, they are naturally robust against motion blur and lighting conditions, which make them ideal for outdoor aerial robot applications. The presented scheme includes two main perception components. First, an asynchronous event-based processing system efficiently detects intrusions by combining several asynchronous event-based algorithms that exploit the advantages of the sequential nature of the event stream. The second is an off-line training mechanism that adjusts the parameters of the event-based algorithms to a particular surveillance scenario and mission. The proposed perception system was implemented in ROS for on-line execution on board UAS, integrated in an autonomous aerial robot architecture, and extensively validated in challenging scenarios with a wide variety of lighting conditions, including day and night experiments in pitch dark conditions.

A DNA Cryptographic Solution for Secured Image and Text Encryption

In recent days, DNA cryptography is gaining more popularity for providing better security to image and text data. This paper presents a DNA based cryptographic solution for image and textual information. Image encryption involves scrambling at pixel and bit levels based on hyperchaotic sequences. Both image and text encryption involves basic DNA encoding rules, key combination, and conversion of data into binary and other forms. This new DNA cryptographic approach adds more dynamicity and randomness, making the cipher and keys harder to break. The proposed image encryption technique presents better results for various parameters, like Image Histogram, Correlation co-efficient, Information Entropy, Number of Pixels Change Rate (NPCR), and Unified Average Changing Intensity (UACI), Key Space, and Sensitivity compared with existing approaches. Improved time and space complexity, random key generation for text encryption prove that DNA cryptography can be a better security solution for new applications.

A Technique for Securing Multiple Digital Images Based on 2D Linear Congruential Generator, Silver Ratio, and Galois Field

Security of multiple digital images over an unsecured channel is a current area of research in the field of image cryptography. Several techniques have been proposed for this purpose. However, the main drawbacks of the existing techniques are as follows: the existing techniques can be applied merely to the grayscale or the truecolor images; the size of the cipher (output) image is bigger than that of the plain (input) images; the authors have used a large number of parameters in the encryption/decryption process, besides the secret key; and the cipher/decipher rounds use the same key for encryption/decryption process. The proposed technique, which is based on a two-dimensional (2D) Linear Congruential Generator (LCG), silver ratio, and Galois field, is designed not merely to secure multiple digital images, namely, three truecolor, nine grayscale, and seventy-two binary images of different sizes, simultaneously, but also to address the above drawbacks. The designed technique achieves both confusion and diffusion properties essential for a secure cryptosystem. Also, the technique is empirically assessed via several statistical and security evaluation metrics, such as key sensitivity, histogram, chi-square, entropy, number of pixel change rate, structural similarity index measure, processing time, etc. The results of these evaluation metrics prove the efficiency and robustness of the designed technique.

Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain

In this paper, a noise-resistant image encryption scheme is proposed. We have used a cubic-logistic map, Discrete Wavelet Transform (DWT), and bit-plane extraction method to encrypt the medical images at the bit-level rather than pixel-level. The proposed work is divided into three sections; In the first and the last section, the image is encrypted in the spatial domain. While the middle section of the proposed algorithm is devoted to the frequency domain encryption in which DWT is incorporated. As the frequency domain encryption section is a sandwich between the two spatial domain encryption sections, we called it a ”sandwich encryption.” The proposed algorithm is lossless because it can decrypt the exact pixel values of an image. Along with this, we have also gauge the proposed scheme’s performance using statistical analysis such as entropy, correlation, and contrast. The entropy values of the cipher images generated from the proposed encryption scheme are more remarkable than 7.99, while correlation values are very close to zero. Furthermore, the number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed encryption scheme is higher than 99.4% and 33, respectively. We have also tested the proposed algorithm by performing attacks such as cropping and noise attacks on enciphered images, and we found that the proposed algorithm can decrypt the plaintext image with little loss of information, but the content of the original image is visible.

Dynamic Substitution and Confusion-Diffusion-Based Noise-Resistive Image Encryption Using Multiple Chaotic Maps

The advancement in wireless communication has encouraged the process of data transferring through the Internet. The process of data sharing via the Internet is prone to several attacks. The sensitive information can be protected from hackers with the help of a process called Encryption. Owing to the increase in cyber-attacks, encryption has become a vital component of modern-day communication. In this article, an image encryption algorithm is suggested using dynamic substitution and chaotic systems. The suggested scheme is based upon the chaotic logistic map, chaotic sine maps and the dynamical substitution boxes (S-boxes). In the proposed scheme, the S-box selection is according to the generated sequence by deploying the chaotic sine map. To evaluate the robustness and security of the proposed encryption scheme, different security analysis like correlation analysis, information entropy, energy, histogram investigation, and mean square error are performed. The keyspace and entropy values of the enciphered images generated through the proposed encryption scheme are over 2278 and 7.99 respectively. Moreover, the correlation values are closer to zero after comparison with the other existing schemes. The unified average change intensity (UACI) and the number of pixel change rate (NPCR) for the suggested scheme are greater than 33, 99.50% respectively. The simulation outcomes and the balancing with state-of-the-art algorithms justify the security and efficiency of the suggested scheme.

An Improved Image Encryption Consuming Fusion Transmutation and Edge Operator

The field of cryptography oversees the development of methods for transforming information between coherent and incoherent formats. Encryption and decryption techniques controlled by keys maintain the privacy of the substance and who can access it. Private key cryptography refers to methods of encryption and decryption that employ the same secret key. The alternative is public key cryptography, wherever the encryption and decryption keys are different. It is essential for the sanctuary of any crypto scheme that the confusion and diffusion properties be met. While the diffusion property rearranges the pixels in an image, the confusion property simply replaces the pixel values. In-depth discussion of a genetic-algorithm-based hybrid approach to secure and complex three-dimensional chaos-based image encryption (SCIE) has been presented. Here, we use mathematics edge, multipoint edges operator, and coupled transmutation operatives to accomplish permutation. In this method, a key stream is created using a 3D CSI (Compound Sine and ICMIC) map. Using a private key, hybrid operators are used to encrypt data. Several metrics were considered while evaluating the suggested algorithm's efficacy, including the UACI (Unified Average Change Intensity), correlation constant, NPCR (Net Pixel Change Rate). Experiments with the same have shown promising results in protecting real-time photos.

MUX induced Ring oscillators for encrypted Nano communication via Quantum Dot Cellular Automata

In the present scenario of Nano communication technology, Quantum Cellular Automata (QCA) is a demanding Quantum concept to communicate information using quantum dots. This paper presents a novel QCA architecture employing majority gates in cross-coupled and cross-oriented structures in QCA designer platform. The selection of the structures in-turn depends on the minimum number of QCA cell usage. In the proposed methodology, an optimised Random Key Generator (RKG) has been constructed using QCA by employing a Linear Feedback Shift Register (LFSR) as a select input to the 2:1 MUX. The MUX generates a random key by selecting either Galois Ring Oscillators (GRO) or Fibonacci Ring Oscillators (FRO). Further, the proposed scheme reduces the Flip flop usage into EXOR gates and wire as compared to the conventional design. The clock circuitry used in the QCA platform would decide the initial seed to the FROs, GROs and the LFSR circuitry in QCA which makes the output Random Key Sequence truly unpredictable. The generated random numbers are used as a key to encode the pixels in the image which enhances the security. Further, the encoded image pixels are encrypted using Deoxyribo Nucleic Acid (DNA) encryption algorithm to render confusion, permutation and diffusion by adopting various rule sets. Image encryption metrics like correlation, entropy, Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI) were computed. Also, power and parameter analysis of the proposed QCA were estimated. From the computed metrics, it is proven that the proposed Nanostructure can be the potential aspirant for the future secured Nano communication.

A new block cipher for image encryption based on multi chaotic systems

In this paper, a new algorithm for image encryption is proposed based on three chaotic systems which are Chen system,logistic map and two-dimensional (2D) Arnold cat map. First, a permutation scheme is applied to the image, and then shuffled image is partitioned into blocks of pixels. For each block, Chen system is employed for confusion and then logistic map is employed for generating subsititution-box (S-box) to substitute image blocks. The S-box is dynamic, where it is shuffled for each image block using permutation operation. Then, 2D Arnold cat map is used for providing diffusion, after that XORing the result using Chen system to obtain the encrypted image.The high security of proposed algorithm is experimented using histograms, unified average changing intensity (UACI), number of pixels change rate (NPCR), entropy, correlation and keyspace analyses.